Max-Sensitivity [1]} is the only metric that isn't designed to evaluate the correctness of an attribution map, but rather the robustness of the attribution map against small perturbations. It does this by adding small perturbations to the sample and recomputing the attribution map on the perturbed samples. The maximum value of the \(L_\infty \) -norm of the difference between the original and perturbed attribution map is measured. To make different attribution methods comparable, the attribution maps are normalized to unit norm before computing Max-Sensitivity.
\(SENS_{MAX}(\mathbf {x}, m, E, c) = \max _{\mathbf {y} - \mathbf {x} \le r} \Vert E(m,\mathbf {x},c) - E(m,\mathbf {y},c) \Vert \) 
